{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA操作总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用测试\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_boston\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# from IPython.display import display\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "# plt.rcParams['font.sans-serif']=['SimHei']\n",
    "# import math\n",
    "# from tqdm import tqdm\n",
    "# import seaborn as sns\n",
    "# import palettable\n",
    "# import datetime\n",
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "X,y = load_boston(return_X_y = True)\n",
    "train= pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Description Function1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====== Description Function =======\n",
    "'''\n",
    "输入:  pandas对象，需要统计的参数\n",
    "\n",
    "输出:  pandas对象，统计结果\n",
    "'''\n",
    "def desc(data, *func):\n",
    "    df = data.agg(['dtype', 'nunique', *func]).T\n",
    "    df['NULL'] = np.round(data.isnull().mean(axis=0)*100, 4)\n",
    "    df = df.reset_index().rename(columns={'index': 'variable'})\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>dtype</th>\n",
       "      <th>nunique</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>NULL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "      <td>504</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>88.9762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>float64</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>float64</td>\n",
       "      <td>76</td>\n",
       "      <td>0.46</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>float64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>float64</td>\n",
       "      <td>81</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>float64</td>\n",
       "      <td>446</td>\n",
       "      <td>3.561</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>float64</td>\n",
       "      <td>356</td>\n",
       "      <td>2.9</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>float64</td>\n",
       "      <td>412</td>\n",
       "      <td>1.1296</td>\n",
       "      <td>12.1265</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>float64</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>float64</td>\n",
       "      <td>66</td>\n",
       "      <td>187</td>\n",
       "      <td>711</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>float64</td>\n",
       "      <td>46</td>\n",
       "      <td>12.6</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>float64</td>\n",
       "      <td>357</td>\n",
       "      <td>0.32</td>\n",
       "      <td>396.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>float64</td>\n",
       "      <td>455</td>\n",
       "      <td>1.73</td>\n",
       "      <td>37.97</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    variable    dtype nunique      min      max  NULL\n",
       "0          0  float64     504  0.00632  88.9762   0.0\n",
       "1          1  float64      26        0      100   0.0\n",
       "2          2  float64      76     0.46    27.74   0.0\n",
       "3          3  float64       2        0        1   0.0\n",
       "4          4  float64      81    0.385    0.871   0.0\n",
       "5          5  float64     446    3.561     8.78   0.0\n",
       "6          6  float64     356      2.9      100   0.0\n",
       "7          7  float64     412   1.1296  12.1265   0.0\n",
       "8          8  float64       9        1       24   0.0\n",
       "9          9  float64      66      187      711   0.0\n",
       "10        10  float64      46     12.6       22   0.0\n",
       "11        11  float64     357     0.32    396.9   0.0\n",
       "12        12  float64     455     1.73    37.97   0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#测试\n",
    "desc(train, 'min', 'max').sort_values(by = 'variable')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Description Function 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 结合官网说明和数据展示，总结出类别型变量名, 连续型变量名和时间序列变量名\n",
    "# contvar_names = ['CPU_USAGE', 'MEM_USAGE', 'LAUNCHING_JOB_NUMS', 'RUNNING_JOB_NUMS', 'SUCCEED_JOB_NUMS', 'CANCELLED_JOB_NUMS', 'FAILED_JOB_NUMS', 'DISK_USAGE']\n",
    "# catvar_names = ['CU', 'STATUS', 'QUEUE_TYPE', 'PLATFORM', 'RESOURCE_TYPE']\n",
    "# timevar_names = ['DOTTING_TIME']\n",
    "\n",
    "contvar_names = [i for i in range(0, 13)]  \n",
    "catvar_names = []\n",
    "timevar_names= []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集：\n",
      "样本数量: 506\n",
      "变量数量（包括因变量）: 13\n",
      "连续型变量 (类型、平均数、极值):\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类型</th>\n",
       "      <th>平均值</th>\n",
       "      <th>最小值</th>\n",
       "      <th>最大值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "      <td>3.613524</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>88.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>float64</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>float64</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>27.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>float64</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>8.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>float64</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>float64</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>12.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>float64</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>float64</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>float64</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>float64</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>396.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>float64</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         类型         平均值        最小值       最大值\n",
       "0   float64    3.613524    0.00632   88.9762\n",
       "1   float64   11.363636    0.00000  100.0000\n",
       "2   float64   11.136779    0.46000   27.7400\n",
       "3   float64    0.069170    0.00000    1.0000\n",
       "4   float64    0.554695    0.38500    0.8710\n",
       "5   float64    6.284634    3.56100    8.7800\n",
       "6   float64   68.574901    2.90000  100.0000\n",
       "7   float64    3.795043    1.12960   12.1265\n",
       "8   float64    9.549407    1.00000   24.0000\n",
       "9   float64  408.237154  187.00000  711.0000\n",
       "10  float64   18.455534   12.60000   22.0000\n",
       "11  float64  356.674032    0.32000  396.9000\n",
       "12  float64   12.653063    1.73000   37.9700"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别型变量(类型、数量)：\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类型</th>\n",
       "      <th>数量</th>\n",
       "      <th>unique_cats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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       "Empty DataFrame\n",
       "Columns: [类型, 数量, unique_cats]\n",
       "Index: []"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "连续型变量分布直方图：\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别型变量分布直方图：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def stat_analysis(data, cont_var_names, cat_var_names):\n",
    "    '''\n",
    "    参数：\n",
    "        1. data           (pd.DataFrame): 输入数据源。\n",
    "        2. cont_var_names (list)        : 连续型变量们的名字。\n",
    "        3. cat_var_names  (list)        : 类别型变量们的名字。\n",
    "    '''  \n",
    "    # 样本数量、变量数量\n",
    "    sample_num, var_num = data.shape\n",
    "    print(\"样本数量: %d\" % sample_num)\n",
    "    print(\"变量数量（包括因变量）: %d\" % var_num)\n",
    "\n",
    "\n",
    "    # 连续型变量 (类型、平均数、极值)\n",
    "    print(\"连续型变量 (类型、平均数、极值):\")\n",
    "    temp_dict = {\"类型\":[], \"平均值\":[], \"最小值\":[], \"最大值\":[]}\n",
    "    names = []\n",
    "    for cont_var_name in cont_var_names:\n",
    "        names.append(cont_var_name)\n",
    "        temp_dict[\"类型\"].append(data.dtypes[cont_var_name])\n",
    "        temp_dict[\"平均值\"].append(np.mean(data[cont_var_name]))\n",
    "        temp_dict[\"最小值\"].append(min(data[cont_var_name]))\n",
    "        temp_dict[\"最大值\"].append(max(data[cont_var_name]))\n",
    "    cont_df = pd.DataFrame(temp_dict, index = names)\n",
    "    display(cont_df)\n",
    "    \n",
    "    # 类别型变量：数量\n",
    "    print(\"类别型变量(类型、数量)：\")\n",
    "    temp_dict = {\"类型\":[], \"数量\":[], \"unique_cats\":[]}\n",
    "    names = []\n",
    "    for cat_var_name in cat_var_names:\n",
    "        names.append(cat_var_name)\n",
    "        temp_dict[\"类型\"].append(data.dtypes[cat_var_name])\n",
    "        temp_dict[\"数量\"].append(len(np.unique(data[cat_var_name].astype(str))))\n",
    "        temp_dict[\"unique_cats\"].append(np.unique(data[cat_var_name].astype(str)))\n",
    "    cat_df = pd.DataFrame(temp_dict, index = names)\n",
    "    display(cat_df)\n",
    "    \n",
    "    # 变量分布图\n",
    "    print(\"连续型变量分布直方图：\")\n",
    "    display_num = len(cont_var_names)\n",
    "    display_var_names = cont_var_names\n",
    "    col_num = 3\n",
    "    row_num = math.ceil(display_num / col_num)\n",
    "    fig = plt.figure(figsize = (12,12))\n",
    "    for i in range(1, display_num + 1):\n",
    "        plt.subplot(row_num, col_num, i)\n",
    "        sns.distplot(data[data[display_var_names[i-1]].notnull()][display_var_names[i-1]])\n",
    "    plt.show()\n",
    "\n",
    "    print(\"类别型变量分布直方图：\")\n",
    "    display_num = len(cat_var_names)\n",
    "    display_var_names = cat_var_names\n",
    "    col_num = 3\n",
    "    row_num = math.ceil(display_num / col_num)\n",
    "    fig = plt.figure(figsize = (12,12))\n",
    "    for i in range(1, display_num + 1):\n",
    "        plt.subplot(row_num, col_num, i)\n",
    "        x = list(data[display_var_names[i-1]].value_counts().index)\n",
    "        y = list(data[display_var_names[i-1]].value_counts().values)\n",
    "        plt.bar(range(len(x)), y)\n",
    "        plt.xticks(range(len(x)), [str(v) for v in x])\n",
    "        plt.xlabel(display_var_names[i-1])\n",
    "        for x_loc, y_loc in zip(range(len(x)), y):\n",
    "            plt.text(float(x_loc)+0.05, float(y_loc)+0.05,'%.2f' % float(y_loc), ha='center',va='bottom')\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "print(\"训练集：\")\n",
    "stat_analysis(train, contvar_names, catvar_names)\n",
    "\n",
    "# print(\"测试集：\")\n",
    "# stat_analysis(test, contvar_names, catvar_names)"
   ]
  }
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